from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-21 14:08:59.717128
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 21, Dec, 2020
Time: 14:09:03
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.9130
Nobs: 147.000 HQIC: -44.9999
Log likelihood: 1574.92 FPE: 1.36310e-20
AIC: -45.7438 Det(Omega_mle): 7.53852e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.453855 0.167885 2.703 0.007
L1.Burgenland 0.143458 0.083302 1.722 0.085
L1.Kärnten -0.235866 0.067223 -3.509 0.000
L1.Niederösterreich 0.104517 0.198945 0.525 0.599
L1.Oberösterreich 0.254489 0.165636 1.536 0.124
L1.Salzburg 0.177691 0.086082 2.064 0.039
L1.Steiermark 0.088456 0.120652 0.733 0.463
L1.Tirol 0.147086 0.079367 1.853 0.064
L1.Vorarlberg 0.006676 0.077279 0.086 0.931
L1.Wien -0.123090 0.162015 -0.760 0.447
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.528559 0.219538 2.408 0.016
L1.Burgenland 0.009629 0.108931 0.088 0.930
L1.Kärnten 0.363171 0.087906 4.131 0.000
L1.Niederösterreich 0.126613 0.260154 0.487 0.626
L1.Oberösterreich -0.187992 0.216597 -0.868 0.385
L1.Salzburg 0.197328 0.112566 1.753 0.080
L1.Steiermark 0.238772 0.157772 1.513 0.130
L1.Tirol 0.143322 0.103786 1.381 0.167
L1.Vorarlberg 0.187334 0.101055 1.854 0.064
L1.Wien -0.588915 0.211862 -2.780 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.296088 0.072639 4.076 0.000
L1.Burgenland 0.103642 0.036042 2.876 0.004
L1.Kärnten -0.025733 0.029085 -0.885 0.376
L1.Niederösterreich 0.074879 0.086077 0.870 0.384
L1.Oberösterreich 0.294057 0.071666 4.103 0.000
L1.Salzburg -0.000625 0.037245 -0.017 0.987
L1.Steiermark -0.029415 0.052202 -0.563 0.573
L1.Tirol 0.089878 0.034340 2.617 0.009
L1.Vorarlberg 0.131362 0.033436 3.929 0.000
L1.Wien 0.072747 0.070099 1.038 0.299
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.179005 0.084314 2.123 0.034
L1.Burgenland -0.007934 0.041836 -0.190 0.850
L1.Kärnten 0.022262 0.033761 0.659 0.510
L1.Niederösterreich 0.015255 0.099913 0.153 0.879
L1.Oberösterreich 0.414551 0.083185 4.983 0.000
L1.Salzburg 0.098477 0.043231 2.278 0.023
L1.Steiermark 0.196961 0.060593 3.251 0.001
L1.Tirol 0.029806 0.039860 0.748 0.455
L1.Vorarlberg 0.102328 0.038811 2.637 0.008
L1.Wien -0.052838 0.081367 -0.649 0.516
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.592630 0.176919 3.350 0.001
L1.Burgenland 0.073536 0.087785 0.838 0.402
L1.Kärnten 0.003830 0.070841 0.054 0.957
L1.Niederösterreich -0.071840 0.209651 -0.343 0.732
L1.Oberösterreich 0.155633 0.174549 0.892 0.373
L1.Salzburg 0.045053 0.090714 0.497 0.619
L1.Steiermark 0.120865 0.127144 0.951 0.342
L1.Tirol 0.219137 0.083638 2.620 0.009
L1.Vorarlberg 0.016518 0.081437 0.203 0.839
L1.Wien -0.138505 0.170733 -0.811 0.417
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.163278 0.122559 1.332 0.183
L1.Burgenland -0.032916 0.060812 -0.541 0.588
L1.Kärnten -0.015451 0.049074 -0.315 0.753
L1.Niederösterreich 0.155671 0.145233 1.072 0.284
L1.Oberösterreich 0.412369 0.120917 3.410 0.001
L1.Salzburg -0.024466 0.062841 -0.389 0.697
L1.Steiermark -0.040959 0.088078 -0.465 0.642
L1.Tirol 0.189278 0.057940 3.267 0.001
L1.Vorarlberg 0.036049 0.056415 0.639 0.523
L1.Wien 0.166502 0.118274 1.408 0.159
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.196803 0.154128 1.277 0.202
L1.Burgenland 0.078623 0.076476 1.028 0.304
L1.Kärnten -0.042856 0.061715 -0.694 0.487
L1.Niederösterreich -0.039990 0.182643 -0.219 0.827
L1.Oberösterreich -0.115572 0.152064 -0.760 0.447
L1.Salzburg 0.011942 0.079028 0.151 0.880
L1.Steiermark 0.392870 0.110765 3.547 0.000
L1.Tirol 0.517235 0.072864 7.099 0.000
L1.Vorarlberg 0.224415 0.070947 3.163 0.002
L1.Wien -0.222010 0.148740 -1.493 0.136
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.098218 0.178056 0.552 0.581
L1.Burgenland 0.031312 0.088348 0.354 0.723
L1.Kärnten -0.115069 0.071296 -1.614 0.107
L1.Niederösterreich 0.176339 0.210998 0.836 0.403
L1.Oberösterreich 0.016923 0.175670 0.096 0.923
L1.Salzburg 0.225724 0.091297 2.472 0.013
L1.Steiermark 0.152825 0.127961 1.194 0.232
L1.Tirol 0.087588 0.084176 1.041 0.298
L1.Vorarlberg 0.038276 0.081961 0.467 0.640
L1.Wien 0.298452 0.171830 1.737 0.082
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.576235 0.099065 5.817 0.000
L1.Burgenland -0.016826 0.049155 -0.342 0.732
L1.Kärnten 0.000195 0.039667 0.005 0.996
L1.Niederösterreich -0.033017 0.117393 -0.281 0.779
L1.Oberösterreich 0.286262 0.097738 2.929 0.003
L1.Salzburg 0.010567 0.050795 0.208 0.835
L1.Steiermark 0.010504 0.071194 0.148 0.883
L1.Tirol 0.076039 0.046833 1.624 0.104
L1.Vorarlberg 0.180662 0.045601 3.962 0.000
L1.Wien -0.085485 0.095602 -0.894 0.371
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.132971 -0.014176 0.193450 0.242940 0.034079 0.089194 -0.120708 0.150011
Kärnten 0.132971 1.000000 -0.021724 0.183752 0.129330 -0.156210 0.168812 0.020625 0.297406
Niederösterreich -0.014176 -0.021724 1.000000 0.257913 0.064758 0.198727 0.089069 0.024021 0.353425
Oberösterreich 0.193450 0.183752 0.257913 1.000000 0.274106 0.275270 0.088719 0.052763 0.079045
Salzburg 0.242940 0.129330 0.064758 0.274106 1.000000 0.143979 0.061854 0.070214 -0.036224
Steiermark 0.034079 -0.156210 0.198727 0.275270 0.143979 1.000000 0.093964 0.066655 -0.163255
Tirol 0.089194 0.168812 0.089069 0.088719 0.061854 0.093964 1.000000 0.130674 0.116626
Vorarlberg -0.120708 0.020625 0.024021 0.052763 0.070214 0.066655 0.130674 1.000000 0.075451
Wien 0.150011 0.297406 0.353425 0.079045 -0.036224 -0.163255 0.116626 0.075451 1.000000